

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 					__lowercase  (						pl.LightningModule				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase						)		->       Any:
														super().__init__()
														A      :					List[Any]      		=					model
														A      :					Union[str, Any]      		=					2
														A      :					Optional[Any]      		=					nn.Linear(self.model.config.hidden_size    ,						self.num_labels						)






							def       snake_case			(					self						)		->       Tuple:
														pass






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							# load longformer model from model identifier
							A      :					Optional[Any]      		=					LongformerModel.from_pretrained(lowerCamelCase_		)
							A      :					str      		=					LightningModel(lowerCamelCase_		)

							A      :					Optional[Any]      		=					torch.load(lowerCamelCase_     ,				map_location=torch.device('''cpu'''		)		)
							lightning_model.load_state_dict(ckpt['''state_dict''']		)

							# init longformer question answering model
							A      :					Any      		=					LongformerForQuestionAnswering.from_pretrained(lowerCamelCase_		)

							# transfer weights
							longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()		)
							longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()		)
							longformer_for_qa.eval()

							# save model
							longformer_for_qa.save_pretrained(lowerCamelCase_		)

							print(F'Conversion successful. Model saved under {pytorch_dump_folder_path}'		)


if __name__ == "__main__":
		lowercase			:				Union[str, Any]   =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--longformer_model",
		    default=None,
		    type=str,
		    required=True,
		    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
		)
		parser.add_argument(
		    "--longformer_question_answering_ckpt_path",
		    default=None,
		    type=str,
		    required=True,
		    help="Path the official PyTorch Lightning Checkpoint.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowercase			:				Optional[int]   =     parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)









import math

import tensorflow as tf
from packaging import version






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Union[str, Any]      		=					0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0		)     ,				x.dtype		)		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(math.pi     ,				x.dtype		)
							A      :					int      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					Tuple      		=					0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi		) * (x + coeff * tf.pow(lowerCamelCase_     ,				3		))		))

							return x * cdf






def        snake_case__		(    lowerCamelCase_		):
							A      :					Union[str, Any]      		=					tf.convert_to_tensor(lowerCamelCase_		)

							return x * tf.tanh(tf.math.softplus(lowerCamelCase_		)		)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					List[Any]      		=					tf.cast(0.044_715     ,				x.dtype		)
							A      :					str      		=					tf.cast(0.7_978_845_608     ,				x.dtype		)

							return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)		))






def        snake_case__		(    lowerCamelCase_		):
							A      :					List[str]      		=					tf.convert_to_tensor(lowerCamelCase_		)
							A      :					Any      		=					tf.cast(1.702     ,				x.dtype		)
							return x * tf.math.sigmoid(coeff * x		)






def        snake_case__		(    lowerCamelCase_		):
							return tf.clip_by_value(_gelu(lowerCamelCase_		)     ,				-10     ,				10		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_=-1		):
							A		,		A      :					Dict      		=					tf.split(lowerCamelCase_     ,				2     ,				axis=lowerCamelCase_		)
							return a * tf.math.sigmoid(lowerCamelCase_		)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):






		def        snake_case__		(    lowerCamelCase_		):
									return tf.keras.activations.gelu(lowerCamelCase_     ,				approximate=lowerCamelCase_		)

		lowercase			:				Tuple   =     tf.keras.activations.gelu

		lowercase			:				Union[str, Any]   =     approximate_gelu_wrap
else:
		lowercase			:				str   =     _gelu
		lowercase			:				Dict   =     _gelu_new


lowercase			:				Union[str, Any]   =     {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}






def        snake_case__		(    lowerCamelCase_		):
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(F'function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}'		)






